Blue Zones Update: the Response

After my post last week about the pre-print paper calling the “Blue Zones” (aka areas with unusual longevity) in to question, an anonymous commenter stopped by to drop the link to the Blue Zones groups response. I thought their response was rather formidable, so I wanted to give it a whole post. They had three major points, all of which I was gratified to see I had raised in my initial read through:

  1. Being designated as a Blue Zone is no small matter, and they have well published criteria. Some places that come to their attention are invalidated. There also are some pretty extensive papers published on how they validated each of the existing 5 regions, which they linked to. Sardinia here, here and here. Okinawa had a paper written on it literally called “They really are that old: a validation study of centenarian prevalence in Okinawa“. Dr Poulain (who did much of the age validation for Sardinia) wrote a paper 10 years ago called “On the age validation of supercentenarians” where he points out that the first concerns about validating supercentenarian ages were raised in 1873. This book has more information about their methods, but notably starts with mentioning 5 regions they were unable to certify. Basically they responded with one big link dump saying “yeah, we thought of that too”. From what I can tell there actually is some really fascinating work being done here, which was very cool to read about. In every place they not only looked at individuals records, crosschecking them with numerous sources, doing personal interviews with people and their families, and then calculating overall population metrics to looks for evidence of fraud. In Okinawa, they mention asking people about the animal for the year of their birth, something people would be unlikely to forget or want to change. It seems pretty thorough to my eye, but I was also struck that none of the papers above were included as references in the original paper. I have no idea if he knew about them or not, but given that he made statements like “these findings raise serious questions about the validity of an extensive body of research based on the remarkable reported ages of populations and individuals.”, it seems like a gap not to include work that had been done.
  2. Supercentenarians are not the focus of the Blue Zones. Again, they publish their criteria, and this is not a focal point. They have focused much more heavily on reaching 90 or 100, particularly with limited chronic disease. As I was scanning through the papers they linked to, I noticed an interesting anecdote about an Okinawan man who for a time was thought to have lived to 120. After he got in the Guinness book of world records, it came out that he had likely been given the name of an older brother who died, and thus was actually “only” 105. This is interesting because it’s a case where his age is fraudulent, but the change wouldn’t impact the “Blue Zone” status.
  3. Relative poverty could be correlated with old age. I raised this point in my initial post, and I was glad to see they echoed it here. Again, most of the way modernity raises life expectancy is by eliminating child mortality and decreasing accidents or repairing congenital defects. Those are the things that will kill you under 55. Over 55, it’s a whole new set of issues.

Now I want to be clear, no one has questioned the fundamental mathematical findings of the paper that in the US the supercentenarian records are probably shaky before birth registration. What’s being questioned is if that finding it’s generalizable to specific areas that have been heavily studied. This is important because in the US “old age” type benefits kick in at 65 and there is no level after that. So basically a random 94 year old claiming to be 111 might get a social bump out of the whole thing, but none of the type of benefits that might have caused people to really look in to it. Once we start getting to things like Blue Zones or international attention though, there’s actually whole groups dedicated to looking in to things. One person faking their age won’t cause much of an issue, but if your claim is that a dozen people in one town are faking their ages, that’s going to start to mess up population curves and show other anomalies. The poorness of the regions actually helps with this case as well. If you’re talking to people in relative poverty with high illiteracy, it’s hard to argue that they could have somehow been criminal masterminds in their forgeries. One or two people can get away with things, but a group deception can be much harder.

I’m still going to keep an eye on this paper, and my guess is it will be published somewhere with some of the suggestions of generalizability toned down, and more references to previous work at validating ages added.

The Evangelical Voter Turnout that (Maybe) Wasn’t

There was an interesting graph in a recent New York Times article  that got Twitter all abuzz:

Visually, this graph is pretty fascinating, showing an increasingly motivated white Evangelical group, whose voter participation rates must put every other group to shame. I was so taken aback by this I actually did share it with a few people as part of a point about voter turnout.

After sharing though, I started to wonder how this turnout rate compared to other religious groups, so I went looking for the source data. A quick Google took me to this Pew Research page, which contained this table:

Two things surprised me about this:

  1. Given the way the data is presented, it appears the Evangelical question was asked by itself as a binary yes/no, as opposed to being part of a list of other options.
  2. The question was not simply “are you Evangelical” but “are you Evangelical/born again”.

Now from researching all sorts of various things for this blog, I happen to know that one of the most common ways of calculating how many white Evangelicals there are in the population is to ask people their denominational affiliation from a menu of choices, then classify those denominations in to Evangelical/Catholic/etc. That’s what PPRI (the group that got the 15% number) does.

For the voting block question however, they were only asked if they were a “White born-again or evangelical Christian?

Now to get too far in to the theological nuances, but there are plenty of folks I know who would claim the “born again” label who don’t go to traditionally “Evangelical” churches.  In fact, according to Mark Silk over at Religion News (who noted this discrepancy at the time), he’s been involved with research that “found that 38.6 percent of mainline Protestants and 18.4 percent of Catholics identified as “born again or evangelical.” So yes, the numbers may be skewed. It’s also worth noting that Pew Research puts the number of Evangelical Protestants at 25%, in a grouping that categorizes historically black groups separately (and thus is presumably mostly white).

So is the Evangelical turnout better than other groups? Well, it might still be. However, it’s good to know that this graph isn’t strictly comparing apples to apples, but rather slightly different questions given to different groups for different purposes. As we know slight changes in questions can yield very different results, so it’s worth noting. Caveat emptor, caveats galore on this one.

Asylum Claims and Other Numbers at the Border

There’s a lot in the news right now about border crossing, immigration and asylum claims, and I’m seeing all sorts of numbers being thrown around on Twitter. I wanted to do a quick round up of some numbers/sources to help people wade through it.

First up, every month US Customs and Border Patrol publishes the number of apprehensions they have at the Southern US Border and how that compares to the last 5 years. They do this relatively close to real time, we have the numbers for May, but not yet for June. If you want to know why you’re seeing so much in the news, take a look at this graph:

So with 4 months left to go in the fiscal year, we’re already 100,000 over the highest year on that chart. To give some context to that though, apprehension numbers have actually been relatively low for the last few years. They peaked at 1.6 million in the late 90s/early 2000s. However, there have been some changes to the makeup of that group….family crossings. Vox published this chart based on the CBP data that shows how this has changed:

I couldn’t find what those numbers were during the last spike, but it seems to be a record high.

So now what about asylum claims? Recently acting DHS Secretary Kevin McAleenan said the 90% of asylum seekers were skipping their hearings, but others were claiming that actually 89% show up. That’s a MASSIVE discrepancy, so I wanted to see what was going on.

First up, the 89% rate. The DOJ publishes all sorts of statistics about asylum hearings, and in this massive report they showed the “in absentia” rates for asylum decisions (page 33):

So for FY17, asylum claimants were at their decision hearing 89% of the time.

So where did the “90% don’t show up” claim come from? Reading the full context of McAleenan’s quote, it appears that he was specifically referencing a new pilot program for families claiming asylum. From what I can tell the pilot program is not published anywhere, so it’s not possible to check the numbers.

So is it plausible it jumped from 11% to 90%? I tend to doubt it, but it’s important to note the lag time here. The last published DOJ numbers are from FY2017, but those are for hearings that took place in FY2017. The average wait time for hearings in these cases for these cases is enormous….727 days so far in 2019.  These wait times are climbing, but if you toggle the graph around, we can see that the wait time back in 2015 was nearly two years:

So essentially those with decisions in FY2017 probably filed in FY2015. And a lot has happened to the stats since then. First, here are the number of applications over the last few years:

So compared to 2015, the number of applications have tripled but the number of approvals has barely budged. We don’t yet know what that will do to the percentage of people who show up, but it seems very plausible that it could increase the absentee rate. Additionally, because family migration is increasing so rapidly, it’s not clear what that will do to the numbers. Regardless, McAleelan’s reference was specifically to that group, so it was only a subset of the numbers that were previously reported. Still, 90% seems awfully high.

Complicating things further of course is the fact that this was a “pilot program”. That means it could have selected just one country or point of entry. One of the more interesting fact sheets from the DOJ site was the rate of asylum approvals by country. In the past few years, here are the top countries (page 29 of this report):

The rates of granting asylum from each of these countries were wildly different in 2018 though. Chinese asylum seekers were 53% granted, El Salvador was 15%, Honduras was 14%, Guatemala was 11%, Mexico was 6%. It seems plausible that a pilot program might have just been addressing those that arrive at the southern border, so it’s possible that individual countries have different profiles.

Overall, it’s clear that the data on this topic is worth watching.

Gender Ratios at Public Events

I’m out of town this weekend indulging in a very non-stats related hobby: pro wrestling.

Those of you who follow such things will know that this is WrestleMania weekend and it’s in New Jersey/just outside of NYC this year, and just so happens to coincide with my husbands birthday. Convenient.

Given the throngs of wrestling fans converging on one spot, there were quite a few other shows put on by other groups looking to capitalize on the crowds. One such show was a New Japan pro wrestling/Ring of Honor joint venture held last night at Madison Square Garden.

We went to this one, and I was interested to note it had one of the more lopsided gender ratios of any event I’ve been to. I’ve mentioned previously that I have a habit of counting such things, and last night was no exception. Normally I like to estimate the ratio of mono-gender groups, but despite all my looking I never found a women only group at this event.

I ended up switching to the number of women I could see in each row – rows were about 15 to 20 seats each. Rows almost always had 2 to 3 women in them. I never found a row with 4 women. I’d estimate the ratio at 7 to 1 male to female.

Other notes:

  • There were more women in the expensive seats than the cheap seats. I’d never explicitly noted uneven distribution of gender before, but I’ll watch for it from now on.
  • The gender ratio changed as the place filled up. When we first got there it was probably closer to 10 to 1, but more women showed up the later it got.
  • Men with long hair were a major confounder. When I count rows from far away I’m mostly looking at hair first, but I had to proceed slowly here.

Obviously those first two bullet points emphasize that this is a male dominated fandom, which I’m sure is not surprising to anyone. This was not a show aimed at the more popular or mainstream fan, but the “willing to tolerate half the show being announced in Japanese” type fan.

Tonight I expect the gender ratio to be more even, as WrestleMania has a broader spectrum of appeal and their women’s division is currently on FIRE. I’ll report back with my estimate tomorrow. Go Becky Lynch!

Update: The gender ratio at Wrestlemania ended up being about 4 or 5 to 1 male:female. Interestingly, the “extra” women were almost entirely younger girls, mostly there with their dad. One ahead of us walking in was even fully in costume (as Bailey), carrying a replica title belt and started trying to lead the “Woooooooooooo” cheer on the escalator. I think the strategy of pumping up their women’s division is paying off.

GPD Lexicon: Broken Record Statistics

Today’s addition to the GPD Lexicon is made in honor of my Dad.

It will come as no surprise to anyone that after years of keeping up this blog, people now have a tendency to seek me out when they have heard a particularly irritating statistic or reference during the week. This week it was my Dad, who heard a rather famous columnist (Liz Bruenig) mention on a podcast that “even conservative New Hampshire is considering getting rid of the death penalty”. He wasn’t irritated at the assertion that NH is looking to get rid of the death penalty (they are), but rather the assertion that NH was representative of a “conservative state”.

You see while NH certainly has a strong Republican presence, it is most famous for being a swing state and has actually gone for Democrats in every presidential election since 1992, except for the year 2000. Currently their Congressional delegation is 4 Democrats. Their state legislature is Democrat controlled. Slightly more people (3%) identify as Democrat or lean that way than Republican. The Governor is a Republican, and it is definitely the most conservative state in New England, but calling it a conservative state on a national level is pretty untrue. Gallup puts it at “average” at best.

What struck me as interesting about this is that New Hampshire actually did used to be more conservative. From 1948 to 1988, a Democrat only won the Presidential election there once. From 1900 to 2000, the Governor was Republican for 84 out years out of the century. In other words, it wasn’t a swing state until around 1992 (per Wiki).

It’s interesting then that Liz Bruenig, born in 1990, would consider NH a conservative state. NH has not been “conservative” in nearly her entire life, so what gives? Why do things like this get repeated and repeated even after they’ve changed? I’ve decided we need a word for this, so my new term is Broken Record Statistics:

Broken Record Statistic: A statistic or characterization that was once true, but is continuously repeated even after the numbers behind it have moved on.

In the course of looking this up btw, I found another possible broken record statistic. If you ask anyone from New Hampshire about the blue shift in the state, they will almost all say it’s because people from Massachusetts are moving up and turning the state more blue. However, the Wiki page I quoted above had this to say “A 2006 University of New Hampshire survey found that New Hampshire residents who had moved to the state from Massachusetts were mostly Republican. The influx of new Republican voters from Massachusetts has resulted in Republican strongholds in the Boston exurban border towns of Hillsborough and Rockingham counties, while other areas have become increasingly Democratic. The study indicated that immigrants from states other than Massachusetts tended to lean Democrat.” The source linked was a Union Leader article (“Hey, don’t blame it on Massachusetts!”) name but no link. Googling showed me nothing. However, the town by town maps do indicate that NH is mostly Republican at the border.

Does anyone know where these numbers are coming from or the UNH study referenced?

Updates on Mortality Rates and the Impact of Drug Deaths

A couple of years ago now, there was a lot of hubbub around a paper about mortality rates among white Americans. This paper purported to show that mortality for middle aged white people in the US were not decreasing (like other countries/races/ethnicities) were, but was actually increasing.

Andrew Gelman and others challenged this idea, and noted that some of the increase in mortality was actually a cohort effect. In other words, mortality was up, but so was the average age of a “45-54 year old”. After adjusting for this, their work suggested that actually it was white middle aged women in the south who were seeing an increase in mortality:

In this article for Slate, they published the state by state data to make this even clearer:

In other words, there are trends happening, but they’re complicated and not easy to generalize.

One of the big questions that came up when this work was originally discussed was how much “despair deaths” like the opioid overdoses or suicide rates were driving this change.

In 2017, a paper was published that showed that this was likely only partially true. Suicide and alcohol related deaths had remained relatively stable for white people, but drug deaths had risen:

Now, there appears to be a new paper coming out  that shows there may be elevated mortality in even earlier age groups. It appears only the abstract is up at the moment, but the initial reporting shows that there may be some increase in Gen X (current 38-45 year olds) and some Gen Y (27-37 year olds). They have reportedly found elevated mortality patterns among white men and women in that age group, being partially driven by drug overdoses and alcohol poisonings.

From the abstract, the generations with elevated mortality were:

    • Non-Hispanic Blacks and Hispanics: Baby Boomers
    • Non-Hispanic White females: late-Gen Xers and early-Gen Yers
    • Non-Hispanic White males: Baby Boomers, late-Gen Xers, and early-Gen Yers.

Partial drivers for each group:

  • Baby Boomers: drug poisoning, suicide, external causes, chronic obstructive pulmonary disease and HIV/AIDS for all race and gender groups affected.
  • Late-Gen Xers and early-Gen Yers: are at least partially driven by mortality related to drug poisonings and alcohol-related diseases for non-Hispanic Whites.

And finally, one nerve-wracking sentence:

Differential patterns of drug poisoning-related mortality play an important role in the racial/ethnic disparities in these mortality patterns.

It remains to be seen if this paper will have some of the cohort effect problems that have plagued other analyses, but the drug poisoning death issue seems to be a common feature. It remains to be seen what the long term outcomes of this will be, but here’s an interesting visualization from Senator Mike Lee’s website:

Not a pretty picture.

Voter Turnout vs Does My Vote Count

Welp, we have another election day coming up. I’ll admit I’ve been a little further removed from this election cycle than most people, for two reasons:

  1. We are undergoing a massive inspection at work tomorrow (gulp) and have been swamped preparing for it. Any thoughts or prayers for this welcome.
  2. I live in a state where most of the races are pretty lopsided.

For point #2, we have Democratic Senator Elizabeth Warren currently up by 22 points, and Republican Governor Charlie Baker currently up by almost 40 points. My rep for the House of Representatives is running unopposed. The most interesting race in our state was actually two Democrats with major streets/bridges named after their families duking it out, but that got settled during the primaries. I’ll vote anyway because I actually have strong feelings about some of our ballot questions, but most of our races are the very definition of “my vote doesn’t make a difference”.

However, I still think there are interesting reasons to vote even if your own personal vote counts minimally. In an age of increasing market segmentation and use of voter files, the demographics that show they consistently vote will always be more catered to by politicians. I mentioned this a while ago in my post about college educated white women. As a group they are only 10% of the voting public, but they are one of the demographics most likely to actually vote, and thus they get more attention than others.

This shows up in some interesting ways. For example, according to Pew Research, during the election Gen Xers and younger will be the majority of eligible voters, yet will not make up the majority of actual voters:

There are race based differences as well. Black voters and white voters vote at similar rates, but Hispanic and Asian voters vote less often.  Additionally, those with more education and those who are richer tend to vote more often.  While that last link mentions that it’s not clear that extra voters would change election results, I still think it’s likely that if some groups with low turnout turned in to groups with high turnout, we may see some changes in messaging.

While this may be mixed for some people who don’t tend to vote with their demographic,  it does seem like getting on the electoral radar is probably a good thing.

So go vote Tuesday!